Design, develop, validate, and maintain workforce models and simulation capabilities to evaluate force structure changes, workforce flows, training pipelines, and readiness impacts, supporting workforce optimization and leadership decision-making.
Compensation
W2 Hourly rate: $45-$55. Pay rates are based on experience, qualifications, location, and certifications, and are subject to change at any time.
Responsibilities
Analyze, integrate, and prepare workforce, training, readiness, and force structure data from sources such as Envision, STARS DMOG, AFMA, ARM, LCOM, and AETC systems.
Develop and maintain workforce flow models, net flow analyses, and workforce health assessments to identify key drivers, bottlenecks, and constraints across the 2A force generation enterprise.
Design, develop, calibrate, and validate simulation models, including Discrete Event Simulation and System Dynamics, to evaluate workforce planning scenarios and force structure alternatives.
Apply quantitative analysis and forecasting techniques to assess workforce trends and readiness impacts.
Develop visualizations, dashboards, technical documentation, and analytical products to communicate findings and support leadership decision-making.
Requirements
Nice to have: Experience with Discrete Event Simulation modeling
Design, develop, test, deploy, and maintain scalable data pipelines using Databricks and related technologies.
Responsibilities
Design, develop, test, deploy, optimize, and support complex, scalable, production-grade data pipelines using Databricks and related data engineering technologies.
Develop robust ETL/ELT solutions to ingest, transform, integrate, and deliver data across cloud and enterprise environments.
Design reusable and maintainable data integration patterns leveraging Databricks, Apache Spark/PySpark, Python, SQL, Delta Lake, APIs, and other appropriate technologies.
Design and maintain efficient data models, data structures, and storage solutions.
Implement data quality, validation, reconciliation, monitoring, and error-handling processes to ensure reliable and accurate data delivery.
Requirements
Bachelor's degree in computer science, Information Systems, Engineering, Data Engineering, or a related field, or equivalent professional experience
Support the installation, operation, maintenance, and continuous improvement of secure IT monitoring infrastructure within a highly regulated and security-sensitive environment.
Locations (5)
Berlin, Germany
Bonn, North Rhine-Westphalia, Germany
Potsdam, Brandenburg, Germany
Neubrandenburg, Mecklenburg-Vorpommern, Germany
Munich, Bavaria, Germany
Compensation
Competitive Compensation: We value our employees and offer an attractive compensation package that reflects your qualifications, experience, and performance.
Architect and implement RAG pipelines, agentic AI systems, and LLM-driven applications for enterprise use cases.
Responsibilities
Architect and implement RAG pipelines, agentic AI systems, and LLM-driven applications for enterprise use cases.
Design and integrate prompt engineering, context management, and knowledge-grounding frameworks to optimize LLM performance.
Collaborate with data, ML, and software engineering teams to build production-grade GenAI microservices and APIs.
Evaluate and integrate open-source and proprietary LLMs (e.g., OpenAI, Anthropic, Mistral, Llama, Gemini).
Design data pipelines for unstructured/structured content ingestion, indexing, and vector retrieval using Milvus, PostgreSQL (pgvector), or similar technologies.
Requirements
Bachelor’s/Master’s degree in Computer Science, AI, Data Engineering, or related field.
Nice to have: multimodal LLMs
Nice to have: agentic reasoning
Nice to have: tool-using AI agents
Nice to have: pharma/life sciences or regulated enterprise domains
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